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Comparing TIGGE multi-model forecasts with. reforecast-calibrated ECMWF ensemble forecasts

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Figure

Table 1: Main features of the nine TIGGE model systems used in this study. BOM:
Figure 1: Illustration of the impact of the verification dataset and bias-correction on  the relative skill of the predictions
Figure 2: Illustration of gain in skill depending on the calibration method applied to  ECMWF  direct  model  output
Figure  3:  Continuous  Ranked  Probability  Skill  Score  versus  lead  time  for  850-hPa  temperature DMO forecasts (left column, subpanel a and c) and 2-m temperature  BC-30  forecasts  (right  column,  subpanel  b  and  d)  in  DJF  2008/09,  averaged
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